An integrated closed-loop solution to assisted history matching and field optimization with machine learning techniques-Reference-Cited by-同舟云学术

An integrated closed-loop solution to assisted history matching and field optimization with machine learning techniques

Published:2021-03 Issue: Volume:198 Page:108204
ISSN:0920-4105
Container-title:Journal of Petroleum Science and Engineering
language:en
Short-container-title:Journal of Petroleum Science and Engineering

Author:

Chai Zhi,Nwachukwu Azor,Zagayevskiy Yevgeniy,Amini Shohreh,Madasu Srinath

Publisher

Elsevier BV

Subject

Geotechnical Engineering and Engineering Geology,Fuel Technology

Reference46 articles.

1. A generalized grid connectivity–based parameterization for subsurface flow model calibration;Bhark;Water Resour. Res.,2011

2. A Comparison of selection schemes used in evolutionary algorithms;Blickle;Evol. Comput.,1996

3. A Tutorial on Bayesian Optimization of Expensive Cost Functions, with Application to Active User Modeling and Hierarchical Reinforcement Learning;Brochu,2010

4. Oil-rate forecast by inferring fractional-flow models from field data with Koval method combined with the capacitance/resistance model;Cao;SPE Reservoir Eval. Eng.,2015

5. An efficient method for fractured shale reservoir history matching: the embedded discrete fracture multi-continuum approach;Chai;J. Petrol. Sci. Eng.,2017

Cited by 24 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. A latent space method with maximum entropy deep reinforcement learning for data assimilation;Geoenergy Science and Engineering;2024-12

2. Graph network surrogate model for subsurface flow optimization;Journal of Computational Physics;2024-09

3. Low salinity water flooding: estimating relative permeability and capillary pressure using coupling of particle swarm optimization and machine learning technique;Scientific Reports;2024-06-08

4. Hybrid Particle Swarm Optimization Coupled by Machine Learning Technique for Estimating Relative Permeability and Capillary Pressure Functions During Low-Salinity Flood;2024-02-02

5. A convolutional neural network-based proxy model for field production prediction and history matching;Gas Science and Engineering;2024-02